Publication Name	World Journal of Methodology
Manuscript ID	105493
Country	United Kingdom

Category	Orthopedics
Manuscript Type	Retrospective Study
Article Title	Machine learning for patient selection in corticosteroid decision making in knee osteoarthritis: A feasibility model
Manuscript Source	Unsolicited Manuscript
All Author List	Omar Musbahi, Kyriacos Pouris, Savvas Hadjixenophontos, Ahmed Al-Saadawi, Iris Soteriou, Justin Peter Cobb and Gareth G Jones
Funding Agency and Grant Number	Funding Agency Grant Number National Institute For Health and Care Research No. NIHR302632
Corresponding Author	Omar Musbahi, Senior Researcher, Department of Surgery and Cancer - Faculty of Medicine, Imperial College London, 86 Wood Ln, London W12 0BZ, United Kingdom. om112@ic.ac.uk
Key Words	Knee osteoarthritis; Machine learning; Predictive modelling; Corticosteroid injection; Patient selection
Core Tip	Historically, the efficacy of corticosteroid injections in knee osteoarthritis has been heavily debated, as patient responses can vary significantly. This study evaluates the feasibility of a machine learning model to identify which patients with knee osteoarthritis will benefit from corticosteroid injections. Data from two cohort studies were combined for analysis. The model generated an accuracy of 67.8% (95% confidence interval: 64.6%-70.9%), F1 score of 30.8%, and an area under the curve score of 0.60. These metrics demonstrate feasibility to assist clinicians with decision-making in patient selection for corticosteroid injections. Further research is required to improve the model prior to testing in clinical settings.
Citation	Musbahi O, Pouris K, Hadjixenophontos S, Al-Saadawi A, Soteriou I, Cobb JP, Jones GG. Machine learning for patient selection in corticosteroid decision making in knee osteoarthritis: A feasibility model. World J Methodol 2025; In press

Received	2025-01-24 06:57
Peer-Review Started	2025-01-24 06:57
To Make the First Decision
Return for Revision	2025-03-16 12:13
Revised	2025-03-29 12:17
Second Decision	2025-05-06 02:51
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-05-07 10:27
Articles in Press	2025-05-07 10:27
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	2222-0682 (online)
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